{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、Python核心语法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 内置数据结构和算法\n",
    "\n",
    "> 1、Python内建类型\n",
    "\n",
    "|分类|类型名称|描述|\n",
    "|----|--------|----|\n",
    "|None|NoneType|null 对象，例如：type(None)|\n",
    "|数值|int|整数|\n",
    "||long|任意精度整数|\n",
    "||float|浮点数|\n",
    "||complex|复数|\n",
    "|序列|str|字符串|\n",
    "||UnicodeType|Unicode字符串|\n",
    "||ListType|列表|\n",
    "||TupleType|元组|\n",
    "||XRangeType|xrange()函数返回的对象|\n",
    "||BufferType|buffer()函数返回的对象|\n",
    "|映射|DictType|字典|\n",
    "|可调用类型|BuiltinFunctionType|内建函数|\n",
    "||BuiltinMethodType|内建方法|\n",
    "||ClassType|类|\n",
    "||FunctionType|用户定义函数|\n",
    "||InstanceType|类实例|\n",
    "||MethodType|Bound class method|\n",
    "||UnboundMethodType|Unbound class method|\n",
    "|模块|ModuleType|模块|\n",
    "|类|ClassType|类定义|\n",
    "|类实例|InstanceType|类实例|\n",
    "|文件|FileType|文件对象|\n",
    "|内部类型|CodeType|字节编译码|\n",
    "||FrameType|执行框架|\n",
    "||TracebackType|异常的堆栈跟踪|\n",
    "||SliceType|由扩展切片操作产生|\n",
    "||EllipsisType|在扩展切片中使用|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 迭代器(Iterator)\n",
    "> 1、可迭代对象：带\\__iter\\__方法的类对象，例如内置类型list、str、tuple、set、frozenset、dict\n",
    "\n",
    "> 2、序列对象：带\\__getitem\\__和\\__len\\__方法的类对象，例如内置类型list、str、tuple\n",
    "\n",
    "> 3、可调用对象: 普通函数、lambda函数、带\\__call\\__方法的类对象\n",
    "\n",
    "> 4、迭代器：带\\__iter\\__、next方法的类对象\n",
    "\n",
    "> 5、iter函数：用于生成迭代器，参数为可迭代对象/序列对象/可调用对象\n",
    "\n",
    "> 6、next函数：用于调用迭代器对象的next方法，参数为迭代器对象\n",
    "\n",
    "> 7、可迭代对象 -> 迭代器 ; 序列对象 -> 迭代器 ; 可调用对象 -> 迭代器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例1\n",
    "from collections import Iterator, Iterable, Callable #测试\n",
    "l = []\n",
    "isinstance(l, Iterable)     #True，list对象是可迭代对象\n",
    "isinstance(l, Iterator)     #False，list对象不是迭代器\n",
    "it = iter(l)                #iter函数把list对象转成迭代器\n",
    "isinstance(it, Iterator)    #True，该对象是迭代器\n",
    "isinstance(it, Iterable)    #True，该对象又是可迭代对象\n",
    "\n",
    "isinstance(lambda x: x+1, Callable) #True，lambda函数是可调用对象\n",
    "isinstance(lambda x: x+1, Iterator) #False，lambda函数不是迭代器\n",
    "it = iter(lambda x: x+1, 5) #iter函数把可调用对象转成迭代器，参数2是终止条件\n",
    "isinstance(it, Iterator)    #True，该对象是迭代器\n",
    "isinstance(it, Iterable)    #True, dir(it),包含__iter__\n",
    "isinstance(it, Callable)    #False, dir(it),已经没有__call__,不在是可调用对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例2\n",
    "from collections import Iterator, Iterable, Callable, Sequence\n",
    "class SubSequence(Sequence):\n",
    "\n",
    "    # 必须重写\n",
    "    def __getitem__(self, index):\n",
    "        pass\n",
    "\n",
    "    # 必须重写\n",
    "    def __len__(self):\n",
    "        pass\n",
    "\n",
    "subseq = SubSequence()\n",
    "isinstance(subseq, Sequence)    #True, 必然是序列对象\n",
    "isinstance(subseq, Iterable)    #True, Sequence包含\\__iter\\__方法\n",
    "it = iter(subseq)               #iter函数把序列对象转成迭代器\n",
    "isinstance(it, Iterator)        #True，该对象是迭代器\n",
    "isinstance(it, Iterable)        #True, dir(it),包含__iter__\n",
    "isinstance(it, Sequence)        #False, 已经不是序列对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例3\n",
    "from collections import Iterable\n",
    "class MyIterable:\n",
    "    #必须定义\n",
    "    def __iter__(self):\n",
    "        pass\n",
    "\n",
    "myiterable = MyIterable()\n",
    "isinstance(myiterable, MyIterable)      #True，可迭代对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例4\n",
    "from collections import Iterator\n",
    "class MyIterator:\n",
    "    #必须定义\n",
    "    def __iter__(self):\n",
    "        pass\n",
    "    #必须定义\n",
    "    def next(self):\n",
    "        pass\n",
    "\n",
    "myiterator = MyIterator()\n",
    "isinstance(myiterator, MyIterator)      #True，迭代器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 生成器(Generators)\n",
    "> 特殊的迭代器, 保存产生迭代元素的动作，而不是所有的迭代元素，更节省内存\n",
    "\n",
    "> 方式一：使用yield关键字的函数\n",
    "\n",
    "> 方式二：生成器表达式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#例1\n",
    "from inspect import isgeneratorfunction, isgenerator\n",
    "from collections import Iterable, Iterator\n",
    "from types import GeneratorType\n",
    "\n",
    "def gen(x):                     #生成器函数\n",
    "    yield x*2\n",
    "print isgeneratorfunction(gen)  #True\n",
    "\n",
    "g2 = (x*2 for x in range(10))   #生成器表达式\n",
    "print isgenerator(g2)           #True\n",
    "print isinstance(g2, Iterator)  #迭代器\n",
    "print isinstance(g2, GeneratorType) #生成器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据编码和处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 面向对象编程\n",
    "> 1、python的类没有访问控制关键字；约定俗成: \\_代表内部实现外部不建议使用，\\__\\*被重命名为 _class__*，无论是属性还是方法\n",
    "\n",
    "> 2、类方法、类静态方法、对象方法；类属性、对象属性；支持单继承与多继承\n",
    "\n",
    "> 3、旧式类、新式类\n",
    "\n",
    "|区别|解释|\n",
    "|-------------------------------|-------------------------------|\n",
    "|\\__new__| 低级别的构造函数，只有新式类支持|\n",
    "|描述符| @property，只有新式类支持，本身也继承Object类|\n",
    "|类函数，静态函数| @classmethod,@staticmethod，只有新式类支持，本身也继承Object类|\n",
    "|MRO|MRO 方法解析次序改变，旧式类多重继承使用的是左递归遍历方式，新式类使用 C3 算法|\n",
    "|super|super指代父类，只有新式类支持|\n",
    "|\\__slot__ | ? |\n",
    "\n",
    "\n",
    "> 4、魔法方法：\n",
    "\n",
    "|魔术方法|调用方式|解释|\n",
    "|-------------------------------|-------------------------------|-------------------------------|\n",
    "|\\__new__(cls [,...])\t| instance = MyClass(arg1, arg2)\t|\\__new__ 低级别的构造函数（新式类）|\n",
    "|\\__init__(self [,...])\t| instance = MyClass(arg1, arg2)\t| \\__init__ 在 new 创建实例后对实例属性初始化的函数|\n",
    "|\\__cmp__(self, other)\t| self == other, self > other, 等。\t| 在比较的时候调用|\n",
    "|\\__pos__(self)\t| +self\t| 一元加运算符|\n",
    "|\\__neg__(self)\t| -self\t| 一元减运算符|\n",
    "|\\__invert__(self)\t| ~self\t| 取反运算符|\n",
    "|\\__index__(self)\t| x[self]\t| 对象被作为索引使用的时候|\n",
    "|\\__nonzero__(self)\t| bool(self)\t| 对象的布尔值|\n",
    "|\\__getattr__(self, name)\t| self.name | # name 不存在\t访问一个不存在的属性时|\n",
    "|\\__setattr__(self, name, val)\t| self.name = val\t| 对一个属性赋值时|\n",
    "|\\__delattr__(self, name)\t| del self.name\t| 删除一个属性时|\n",
    "|\\__getattribute(self, name)\t| self.name\t| 访问任何属性时,新式类|\n",
    "|\\__getitem__(self, key)\t| self[key]\t| 使用索引访问元素时|\n",
    "|\\__setitem__(self, key, val)\t| self[key] = val\t| 对某个索引值赋值时|\n",
    "|\\__delitem__(self, key)\t| del self[key]\t| 删除某个索引值时|\n",
    "|\\__iter__(self)\t| for x in self\t| 迭代时|\n",
    "|\\__contains__(self, value)\t| value in self, value not in self\t| 使用 in 操作测试关系时|\n",
    "|\\__concat__(self, value)\t| self + other\t| 连接两个对象时|\n",
    "|\\__call__(self [,...])\t| self(args)\t| “调用”对象时|\n",
    "|\\__enter__(self)\t| with self as x:\t| with 语句环境管理|\n",
    "|\\__exit__(self, exc, val, trace)\t| with self as x:\t| with 语句环境管理|\n",
    "|\\__getstate__(self)\t| pickle.dump(pkl_file, self)\t| 序列化|\n",
    "|\\__setstate__(self)\t| data = pickle.load(pkl_file)\t| 序列化|\n",
    "|\\__metaclass__||参考《元编程》|\n",
    "\n",
    "\n",
    "> 5、\\__slots__属性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 元编程\n",
    "> 1、python中一切都是对象，类也不例外;解释器在解析代码时候，根据类定义自动生成对象（不是类实例对象）\n",
    "\n",
    "> 2、元编程是一种自由的创建、动态修改，删除类的技术\n",
    "\n",
    "> 3、元类type，一起类的鼻祖；使用type来组装类，type(类名, 父类的元组(针对继承的情况，可以为空)，包含属性的字典(名称和值))\n",
    "\n",
    "> 4、\\__metaclass__\n",
    "\n",
    "> 5、猴子补丁\n",
    "\n",
    "> 6、装饰器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模块与包"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 并发编程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、Python标准库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 集合工具collections\n",
    "> 可迭代对象\n",
    "\n",
    "> 可调用对象\n",
    "\n",
    "> 迭代器\n",
    "\n",
    "> 容器对象\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 迭代工具itertools\n",
    "> 无限迭代器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#例1\n",
    "from itertools import count, cycle, repeat, chain, izip, ifilterfalse, groupby, count\n",
    "\n",
    "itloop = count(10,1)    #开始10，步长1； 10，11，12，...\n",
    "itloop = cycle('ABCD')  #开始'A', 周期； ‘A’,‘B’,‘C’,‘D’,‘A’,‘B’,‘C’,‘D’,...\n",
    "itloop = repeat(1, 5)   #开始1，循环5次，如果参数为空，无限循环下去; 1.1.1.1..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间与日期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1453381513.22\n",
      "7.60261727702e-07\n",
      "time.struct_time(tm_year=2016, tm_mon=1, tm_mday=21, tm_hour=13, tm_min=5, tm_sec=13, tm_wday=3, tm_yday=21, tm_isdst=0)\n",
      "time.struct_time(tm_year=2016, tm_mon=1, tm_mday=21, tm_hour=21, tm_min=5, tm_sec=13, tm_wday=3, tm_yday=21, tm_isdst=0)\n",
      "1453381513.0\n",
      "--------------------\n",
      "2013-02-03 12:30:59\n",
      "2013-02-03\n",
      "12:30:59\n",
      "2013-02-03 12:30:59\n",
      "2016-01-21 21:05:13.223000\n",
      "Thu Jan 21 21:05:13 2016\n",
      "2016-01-21\n",
      "--------------------\n",
      "2016-01-26\n",
      "2016-01-22 02:05:13.224000\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "#例1\n",
    "import time, datetime\n",
    "\n",
    "print time.time()                               #当前秒数\n",
    "print time.clock()                              #系统启动秒数\n",
    "print time.gmtime()                             #格林威治(GMT)时间\n",
    "print time.localtime()                          #当前本地时间\n",
    "print time.mktime(time.localtime())             #当前本地时间 -> 秒数\n",
    "#time.sleep(1)                                  #休眠\n",
    "print '--------------------'\n",
    "\n",
    "print datetime.datetime(2013, 2, 3, 12, 30, 59) #datetime对象\n",
    "print datetime.date(2013, 2, 3)                 #date日期对象，年，月，日\n",
    "print datetime.time(12, 30, 59)                 #time时间对象，小时，分钟，秒\n",
    "print datetime.datetime.combine(datetime.datetime(2013, 2, 3, 12, 30, 59), datetime.time(12, 30, 59)) #日期对象 + 时间对象 =》 datetime\n",
    "print datetime.datetime.now()                   #当前datetime\n",
    "print datetime.datetime.now().ctime()           #当前datetime,ctime格式\n",
    "print datetime.date.today()                     #当前date日期对象\n",
    "print '--------------------'\n",
    "\n",
    "print datetime.timedelta(days=5) + datetime.date.today()    #5天后，imedelta用于datetime、date、time对象计算\n",
    "print datetime.timedelta(hours=5) + datetime.datetime.now() #5小时后\n",
    "print '--------------------'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、Python数据结构与算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 排序算法\n",
    "### 稳定的排序\n",
    "#### 冒泡排序（bubble sort）— O(n2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "Wall time: 0 ns\n",
      "[5, 4, 6, 3, 7, 2, 8, 1, 9, 0]\n",
      "Wall time: 0 ns\n",
      "[0, 1, 2, 3, 4, 5, 7, 8, 9, 6]\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def sort_bubble(A):\n",
    "    \"\"\"冒泡排序\n",
    "    \"\"\"\n",
    "    len_A = len(A)\n",
    "    assert len_A>=2\n",
    "    \n",
    "    for pos in range(1,len_A-1):\n",
    "        for index in range(pos): #与插入排序的差异\n",
    "            if A[index] > A[index+1]:\n",
    "                A[index], A[index+1] = A[index+1], A[index]\n",
    "    print A\n",
    "\n",
    "A = range(10)                        #最好情况\n",
    "%time sort_bubble(A)\n",
    "A.reverse()                            #最坏情况\n",
    "%time sort_bubble(A)\n",
    "A = random.sample(A, len(A))           #平均情况\n",
    "%time sort_bubble(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 鸡尾酒排序（cocktail sort）—O(n2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 插入排序（insertion sort）—O(n2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "Wall time: 0 ns\n",
      "[9, 1, 2, 3, 4, 5, 6, 7, 8, 0]\n",
      "Wall time: 0 ns\n",
      "[0, 1, 2, 3, 4, 5, 6, 7, 9, 8]\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def sort_insertion(A):\n",
    "    \"\"\"插入排序 \n",
    "    \"\"\"\n",
    "    len_A = len(A)\n",
    "    assert len_A>=2\n",
    "    \n",
    "    for pos in range(1,len_A-1):\n",
    "        key = A[pos]\n",
    "        i = pos - 1\n",
    "        while i>0 and A[i]>key:\n",
    "            A[i+1] = A[i]\n",
    "            i = i -1\n",
    "        A[i+1] = key\n",
    "    print A\n",
    "\n",
    "\n",
    "A = range(10)                         #最好情况\n",
    "%time sort_insertion(A)\n",
    "A.reverse()                            #最坏情况\n",
    "%time sort_insertion(A)\n",
    "A = random.sample(A, len(A))           #平均情况\n",
    "%time sort_insertion(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 桶排序（bucket sort）—O(n);需要O(k)额外空间\n",
    "#### 计数排序（counting sort）—O(n+k);需要O(n+k)额外空间\n",
    "#### 归并排序（merge sort）—O(n log n);需要O(n)额外空间\n",
    "#### 原地归并排序— O(n2)\n",
    "#### 二叉排序树排序（binary tree sort）— O(n log n)期望时间; O(n2)最坏时间;需要O(n)额外空间\n",
    "#### 鸽巢排序（pigeonhole sort）—O(n+k);需要O(k)额外空间\n",
    "#### 基数排序（radix sort）—O(n·k);需要O(n)额外空间\n",
    "#### 侏儒排序（gnome sort）— O(n2)\n",
    "#### 图书馆排序（library sort）— 时间复杂度通常是O(n log n),需要(1+ε)n额外空间\n",
    "### 不稳定的排序\n",
    "#### 选择排序（selection sort）—O(n2)\n",
    "#### 希尔排序（shell sort）—O(n log2 n)如果使用最佳的现在版本\n",
    "#### Clover排序算法（Clover sort）—O(n)期望时间，O（n^2/2）最坏情况\n",
    "#### 梳排序— O(n log n)\n",
    "#### 堆排序（heap sort）—O(n log n)\n",
    "#### 平滑排序（smooth sort）— O(n log n)\n",
    "#### 快速排序（quick sort）—O(n log n)期望时间, O(n2)最坏情况;对于大的、乱数列表一般相信是最快的已知排序\n",
    "#### 内省排序（introsort）—O(n log n)\n",
    "#### 耐心排序（patience sort）—O(n log n + k)最坏情况时间，需要额外的O(n + k)空间，也需要找到最长的递增子序列（longest increasing subsequence）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 四、Python Sympy数学符号库\n",
    " >Symbol定义符号\n",
    " \n",
    " > deltaΘ符号\n",
    " \n",
    " >limit极限\n",
    " \n",
    " >diff(func, var)求导"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 极限"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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23TV2EsmGK66Ahx6CTz+NnURyIdMefQdgmbuvcPdNwCNA91ra9QMmAGszvJ4UgM2b4YEH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\nEUk4FXoRkYRToRcRSTgVehGRhPv/IjHOsoWoVaUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9b62d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from sympy import *\n",
    "x = Symbol(\"x\")                 #定义符号\n",
    "y = limit(sin(x)/x, x, 0)       #1，limit极限函数\n",
    "\n",
    "#sin(x)/x图形\n",
    "import numpy as np\n",
    "xn = np.linspace(-5, 5, num = 100)\n",
    "yn = np.sin(xn)/xn\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(xn,yn)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cos(x)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import diff, Symbol, sin\n",
    "x = Symbol('x')\n",
    "diff(sin(x), x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 五、Python Numpy库\n",
    "> array数组对象，包括实际数据和描述数据的元数据\n",
    "\n",
    "> arange()函数\n",
    "\n",
    "> resize()函数，改变纬度，直接操作数组\n",
    "\n",
    "> reshape()函数，改变纬度\n",
    "\n",
    "> .shape属性，同上\n",
    "\n",
    "> ravel()函数，展平变为一维，仅返回视图\n",
    "\n",
    "> flatten()函数，展平变为一维，重新分配内存\n",
    "\n",
    "> transpose()函数，矩阵转置\n",
    "\n",
    "> vstack、dstack、hstack、column_stack、row_stack以及concatenate函数，数组水平、垂直组合\n",
    "\n",
    "> hsplit、vsplit、dsplit和split函数，数组分割\n",
    "\n",
    "> 矩阵计算函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## array数组对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.        , -4.47368421, -3.94736842, -3.42105263, -2.89473684,\n",
       "       -2.36842105, -1.84210526, -1.31578947, -0.78947368, -0.26315789,\n",
       "        0.26315789,  0.78947368,  1.31578947,  1.84210526,  2.36842105,\n",
       "        2.89473684,  3.42105263,  3.94736842,  4.47368421,  5.        ])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr1 = np.arange(9)                   #数组对象array([0, 1, 2, 3, 4...]),9个元素\n",
    "arr1[2:]                              #切片\n",
    "arr1[::-1]                            #反序\n",
    "arr[1::2]                             #pos1, step2\n",
    "arr1.reshape(1,3,3)                   #重新改变纬度\n",
    "\n",
    "arr2 = np.linspace(-5, 5, num = 20)  #数组对象array([-5,...5], 均匀分布20个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 六、Python Matplotlib统计绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 5, 6]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f193810>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f171370>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1713f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1933b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1935d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f193810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例1\n",
    "figure()       #创建第一个绘图对象\n",
    "figure(2)      #创建第二个绘图对象\n",
    "figure(3)      #创建第三个绘图对象\n",
    "figure(5)      #创建第四个绘图对象\n",
    "figure()       #创建第五绘图对象\n",
    "print get_fignums()  #返回对象个数\n",
    "gcf()          #f返回当前绘图对象，这里指向第一个"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建子图和子区"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0qofqEnAJsCfJxacYdztwL7DpfxpLi2LNXb3UpIfq8PPPAP8PuAL4blV9a519WXPXzFhz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\n2ziHW1eD9LgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f310e30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例1\n",
    "subplot(221)   #创建2x2格子1子区，默认创建绘图对象\n",
    "subplot(224)   #创建2x2格子4子区，在已有绘图对象中\n",
    "grid()         #当前子区中创建网格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c24aa30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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23CVphdlzlyRtsLgvgf3EhrlomIuGuZidxV2SesieuyStMHvukqQNncxQHa67NMmxJB/o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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20fb57d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例2\n",
    "subplots()      #创建1个子区，默认创建绘图对象\n",
    "subplots(2)     #创建2个子区，同时创建新的绘图对象，注意与subplot区别\n",
    "grid()          #当前子区中创建网格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1c2aee90>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIgBYcH+dJIKZ9QBecffTIxwbBkxw92Fmdi5wv7ufG2GckxuDYOYCtW8dOqgA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\nZmZmTcYDOTMzMzMzsybz/5oMZJBDK/MJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf1ded0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例3\n",
    "axes([.65, .6, .2, .2])  #默认创建绘图对象，创建子图,[left, bottom, width, height]\n",
    "axes([.75, .7, .2, .2])  #相同绘图对象中，再次创建子图\n",
    "axes([.85, .8, .2, .2], axisbg='g')           #属性，设置背景色\n",
    "axes([.95, .9, .2, .2], frameon=False)        #属性，不显示外框\n",
    "axes([1.25, .9, .2, .2], polar=True)          #属性，极坐标\n",
    "axes([1.55, .6, .6, .9], aspect='equal')      #属性，y/x比例，equal、auto、number\n",
    "axes([2.15, .6, .6, .9], aspect=2)            #属性，y/x比例，2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.PolarAxesSubplot at 0x1ba04a70>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kkYgMDvjuHsdZwSoRucC/UisiI0TkSxH5ZyPXsTLLF5GeIhLWXYiItBMRK7f8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\nGts3TBr1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e8b9f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例4\n",
    "subplot(221,frameon=False,aspect=2,polar=True,axisbg='g')  #axes属性，同样适用于subplot,但不适用于subplots\n",
    "subplot(222,frameon=True,aspect=2,polar=True,axisbg='w')\n",
    "subplot(223,frameon=True,aspect=2,polar=True,axisbg='r')\n",
    "subplot(224,frameon=True,aspect=2,polar=True,axisbg='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 快速绘图函数 - plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1547f9d0>]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nKq+JcznT6Aza4ngJ8J3M/F5m7gP+GXjdUgcuLsLLXgY339yt5o0b6z1xaxP/QZypHGcq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\n0JLUUP8PidfcScI7Cj0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e6e450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(np.arange(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot图类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直方图（又称柱状图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2.,   1.,   3.,   2.,   5.,   6.,  10.,  10.,   7.,   8.,   9.,\n",
       "          8.,   4.,   5.,   5.,   1.,   2.,   2.,   4.,   0.,   2.,   0.,\n",
       "          1.,   1.,   2.]),\n",
       " array([-1.9317855 , -1.75389272, -1.57599993, -1.39810715, -1.22021436,\n",
       "        -1.04232158, -0.86442879, -0.68653601, -0.50864323, -0.33075044,\n",
       "        -0.15285766,  0.02503513,  0.20292791,  0.3808207 ,  0.55871348,\n",
       "         0.73660626,  0.91449905,  1.09239183,  1.27028462,  1.4481774 ,\n",
       "         1.62607019,  1.80396297,  1.98185576,  2.15974854,  2.33764132,\n",
       "         2.51553411]),\n",
       " <a list of 25 Patch objects>)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEACAYAAACTXJylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4AaAZAhuAEiG4AaAZP4P1cQTvteICAEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21061ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.random.randn(100)\n",
    "hist(y, 10)\n",
    "hist(y, 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直条图（又称条形图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI7VMLSzIevcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ff371f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([1, 2, 3], [3, 2, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x29082150>,\n",
       "  <matplotlib.patches.Wedge at 0x2902ccf0>,\n",
       "  <matplotlib.patches.Wedge at 0x28fee990>],\n",
       " [<matplotlib.text.Text at 0x29077810>,\n",
       "  <matplotlib.text.Text at 0x28feef70>,\n",
       "  <matplotlib.text.Text at 0x28fee390>])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iBbQDRmRXyJ2eUvR/BTxDwBsQ+DWwpMCidNES+EUDBqsTRsdEjxdzfeloXFwB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\nJEF5g10AAAAMSURBVACAQiQBABTv1YqW/gZQ4sUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28d9ea70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [45, 35, 20]                    #比例\n",
    "figure(figsize=(10,8))\n",
    "subplot(341)\n",
    "labels = ['Cats', 'Dogs', 'Fishes']\n",
    "pie(x, labels=labels)              #属性，标签\n",
    "subplot(342)\n",
    "pie(x, colors=['w','w','w'])       #属性，颜色\n",
    "subplot(343)\n",
    "pie(x, shadow=True)                #属性，阴影，不明显"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 七、Python PIL图像处理\n",
    "## 基本操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(580, 560) JPEG RGB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x4f8d3150>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Dnp4T74Z5Nuzfv1/mJS3LolgsMjo6epy+y8KCcjKZPCXf+1zwdF5FT7Hg1XEcQqEQ//iP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\nFIN5QUFBwSmgGMwLCgoKTgH/PwuAoBzuOQnzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4a4ca430>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mkKW4e55XHLcHuOOOOwpHihRz4Afy7TI9k2UpYRAikpiw22KjC0YWsXnCIg58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Wa7Tbq9gwwtvFabIApAKcqyos4KlKqxpYvf8CinCr8RM7gwolKSrc09qromcCUL8w3cwKcsS7Yr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4cb54b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VVXzyk5886TEUyVXBqzDic508EYY5Xl5ftcZc9LY+Hih6DLzZJkfPbrlDrn3f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OzsDbl6/YDhYchbr7yCriYMD+4TBaMj5/rd7nzFn3+QkD9JHC33KwR2Op3S7/cZj8fous6nPvUp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\nwFzM58yZM+cEMBfzOXPmzDkB/H/klvWZEsTIIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4cf22870>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AH2QdT7d713H/YMGtm/t413F4fxdJVDV98IPPxSpFK5586kkuXrw4eIx+Rx4d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\ngmt+BkZ1uBQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x502ca1b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from PIL import Image\n",
    "from pylab import *\n",
    "import matplotlib.cm\n",
    "from numpy import *\n",
    "im = Image.open('doc/emac.jpg')\n",
    "\n",
    "plt.figure()\n",
    "#原图\n",
    "plt.subplot(131)\n",
    "plt.axis('off')\n",
    "plt.imshow(im)\n",
    "print im.size, im.format, im.mode    #查看图片的各种属性\n",
    "#灰度图\n",
    "plt.subplot(132)\n",
    "plt.axis('off')\n",
    "gray_im1 = array(im.convert(\"L\"))\n",
    "plt.imshow(gray_im1,cmap=matplotlib.cm.Greys_r)\n",
    "plt.subplot(133)\n",
    "plt.axis('off')\n",
    "plt.gray()\n",
    "gray_im2 = array(im.convert(\"L\"))\n",
    "plt.imshow(gray_im2)\n",
    "\n",
    "plt.figure()\n",
    "#切分\n",
    "plt.subplot(131)\n",
    "plt.axis('off')\n",
    "c = (100, 100, 500, 500)\n",
    "crop_im = im.crop(box)\n",
    "plt.imshow(crop_im)\n",
    "#反转\n",
    "plt.subplot(132)\n",
    "plt.axis('off')\n",
    "transpose_im = im.transpose(Image.ROTATE_180)\n",
    "plt.imshow(transpose_im)\n",
    "#调整大小\n",
    "plt.subplot(133)\n",
    "plt.axis('off')\n",
    "resize_im = im.resize((200,500))\n",
    "plt.imshow(resize_im)\n",
    "\n",
    "plt.figure()\n",
    "#旋转\n",
    "plt.subplot(131)\n",
    "plt.axis('off')\n",
    "rotate_im = im.rotate(45)\n",
    "plt.imshow(rotate_im)\n",
    "#通道分离\n",
    "plt.subplot(132)\n",
    "plt.axis('off')\n",
    "im.getdata()\n",
    "r,g,b = im.split()    #分离三个通道\n",
    "plt.imshow(r)\n",
    "#通道合并\n",
    "plt.subplot(133)\n",
    "plt.axis('off')\n",
    "merge_im = Image.merge(\"RGB\", (r, g, b))\n",
    "plt.imshow(merge_im)  #合并生成原图\n",
    "\n",
    "plt.figure()\n",
    "#合并图片\n",
    "plt.subplot(131)\n",
    "plt.axis('off')\n",
    "im2 = Image.open('doc/me.jpg').resize((200,500))\n",
    "blend_im = Image.blend(resize_im, im2, 0.5)\n",
    "plt.imshow(blend_im)\n",
    "#粘贴\n",
    "#plt.subplot(132)\n",
    "#im2 = Image.open('doc/me.jpg')\n",
    "#paste_pos = (10, 10, 10, 10)\n",
    "#im2.paste(im, box=paste_pos)\n",
    "#plt.imshow(im2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 频域处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图像复原"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 形态学"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图像分割"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 八、Python Scikit机器学习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、K近邻法\n",
    "------------------- \n",
    "        \n",
    "    1、k近邻法不具有显示的学习过程，而是利用训练集对特征空间进行划分,并作为其分类的模型\n",
    "    2、"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、决策树\n",
    "------------------- "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
